Reconstructing genome-scale metabolic models with merlin

Dias, Óscar; Rocha, Miguel; Ferreira, Eugénio C.; Rocha, Isabel

doi:10.1093/nar/gkv294

Cited by 123 publications

(103 citation statements)

References 43 publications

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“…Whereas smaller metabolic models, for instance, of the central metabolism, are often compiled from established biochemical “text book” knowledge, the reconstruction of genome-scale models (covering the entire metabolism of small metabolites) was greatly facilitated due to the availability of sequenced genomes for many organisms4567. Several tools have been developed to support the reconstruction and gap filling process to eventually obtain high-quality genome-scale models89101112. It has also become an important task to maintain and continually improve the quality of existing models which is well exemplified by the evolution of genome-scale metabolic models of Escherichia coli .…”

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confidence: 99%

EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model

Hädicke

Klamt

2017

Sci Rep

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Genome-scale metabolic modeling has become an invaluable tool to analyze properties and capabilities of metabolic networks and has been particularly successful for the model organism Escherichia coli. However, for several applications, smaller metabolic (core) models are needed. Using a recently introduced reduction algorithm and the latest E. coli genome-scale reconstruction iJO1366, we derived EColiCore2, a model of the central metabolism of E. coli. EColiCore2 is a subnetwork of iJO1366 and preserves predefined phenotypes including optimal growth on different substrates. The network comprises 486 metabolites and 499 reactions, is accessible for elementary-modes analysis and can, if required, be further compressed to a network with 82 reactions and 54 metabolites having an identical solution space as EColiCore2. A systematic comparison of EColiCore2 with its genome-scale parent model iJO1366 reveals that several key properties (flux ranges, reaction essentialities, production envelopes) of the central metabolism are preserved in EColiCore2 while it neglects redundancies along biosynthetic routes. We also compare calculated metabolic engineering strategies in both models and demonstrate, as a general result, how intervention strategies found in a core model allow the identification of valid strategies in a genome-scale model. Overall, EColiCore2 holds promise to become a reference model of E. coli’s central metabolism.

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confidence: 99%

EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model

Hädicke

Klamt

2017

Sci Rep

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“…Reconstructing GEMs once involved a laborious manual process that took at least 6 months per microbial species [42], but today automatic metabolic reconstruction tools such as the RAVEN Toolbox [52], Pathway Tools [53], MicrobesFlux [54], merlin [55], and ModelSEED [56] have significantly reduced the time and effort required. ModelSEED, the most widely used tool, automatically reconstructs metabolic models for bacterial species with full genome sequences, and gap-fills them using a parsimonious approach in which the least number of reactions is added to complete a particular pathway.…”

Section: Metabolic Models Of the Gut Microbiomementioning

confidence: 99%

Community metabolic modeling approaches to understanding the gut microbiome: Bridging biochemistry and ecology

Mendes‐Soares

Chia

2017

Free Radical Biology and Medicine

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Interest in the human microbiome is at an all time high. The number of human microbiome studies is growing exponentially, as are reported associations between microbial communities and disease. However, we have not been able to translate the ever-growing amount of microbiome sequence data into better health. To do this, we need a practical means of transforming a disease-associated microbiome into a health-associated microbiome. This will require a framework that can be used to generate predictions about community dynamics within the microbiome under different conditions, predictions that can be tested and validated. In this review, using the gut microbiome to illustrate, we describe two classes of model that are currently being used to generate predictions about microbial community dynamics: ecological models and metabolic models. We outline the strengths and weaknesses of each approach and discuss the insights into the gut microbiome that have emerged from modeling thus far. We then argue that the two approaches can be combined to yield a community metabolic model, which will supply the framework needed to move from high-throughput omics data to testable predictions about how prebiotic, probiotic, and nutritional interventions affect the microbiome. We are confident that with a suitable model, researchers and clinicians will be able to harness the stream of sequence data and begin designing strategies to make targeted alterations to the microbiome and improve health.

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“…While the reconstruction of draft genomescale metabolic models for individual species is largely automated today using resources like the ModelSEED, MERLIN, Raven Toolbox, or KBase [27,31,32], draft models often fall short of accurate predictions of species behavior, due to thermodynamically infeasible pathways, errors in annotations, missing annotations, or errors in biomass composition reactions. These issues must be The reconstruction process begins with genome annotation, which involves the assignment of functions to genes, and may also include the calling of the genes themselves.…”

Section: Reconstruction and Curation Of A Genome-scale Metabolic Modelmentioning

confidence: 99%

Constructing and Analyzing Metabolic Flux Models of Microbial Communities

Faria

Khazaei

Edirisinghe

et al. 2016

Springer Protocols Handbooks

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Here we provide a broad overview of current research in modeling the growth and behavior of microbial communities, while focusing primarily on metabolic flux modeling techniques, including the reconstruction of individual species models, reconstruction of mixed-bag models, and reconstruction of multi-species models. We describe how flux balance analysis may be applied with these various model types to explore the interactions of a microbial community with its environment, as well as the interactions of individual species with each other. We demonstrate all discussed model reconstruction and analysis approaches using the Department of Energy's Systems Biology Knowledgebase (KBase), constructing and importing genomescale metabolic models of Bacteroides thetaiotaomicron and Faecalibacterium prausnitzii, and subsequently combining them into a community model of the gut microbiome. We also use KBase to explore how these species interact with each other and with the gut environment, exploring the trade-offs in information provided by applying each metabolic flux modeling approach. Overall, we conclude that no single approach is better than the others, and often there is much to be gained by applying multiple approaches synergistically when exploring the ecology of a microbial community.

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Reconstructing genome-scale metabolic models with merlin

Cited by 123 publications

References 43 publications

EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model

EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model

Community metabolic modeling approaches to understanding the gut microbiome: Bridging biochemistry and ecology

Constructing and Analyzing Metabolic Flux Models of Microbial Communities

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